Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.
| Version: | 0.10.0 |
| Depends: | R (≥ 3.6.0) |
| Imports: | abind,fMRItools (≥ 0.5.3),fMRIscrub (≥ 0.14.5),foreach,ica,Matrix,matrixStats, methods,pesel,SQUAREM, stats, utils |
| Suggests: | ciftiTools (≥ 0.13.2),excursions,RNifti,oro.nifti,gifti,covr, parallel,doParallel,knitr,rmarkdown, INLA,testthat (≥ 3.0.0) |
| Published: | 2025-05-19 |
| DOI: | 10.32614/CRAN.package.templateICAr |
| Author: | Amanda Mejia [aut, cre], Damon Pham [aut], Daniel Spencer [ctb], Mary Beth Nebel [ctb] |
| Maintainer: | Amanda Mejia <mandy.mejia at gmail.com> |
| BugReports: | https://github.com/mandymejia/templateICAr/issues |
| License: | GPL-3 |
| URL: | https://github.com/mandymejia/templateICAr |
| NeedsCompilation: | no |
| Additional_repositories: | https://inla.r-inla-download.org/R/testing |
| Citation: | templateICAr citation info |
| Materials: | README,NEWS |
| CRAN checks: | templateICAr results |